# PeaTMOSS: Mining Pre-Trained Models in Open-Source Software

arXiv: https://arxiv.org/abs/2310.03620 · HTML: https://mlsanity.com/paper/2310.03620/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 36 references that are in the map.
- First posted on arXiv 5 Oct 2023.

## Builds on (most cited first, 19 of 19)

- [On Testing Machine Learning Programs](https://mlsanity.com/paper/1812.02257/) (2018)
- [An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registry](https://mlsanity.com/paper/2303.02552/) (2023)
- [Backdoor Attacks against Transfer Learning with Pre-trained Deep Learning Models](https://mlsanity.com/paper/2001.03274/) (2020)
- [ModelDiff: Testing-Based DNN Similarity Comparison for Model Reuse Detection](https://mlsanity.com/paper/2106.08890/) (2021)
- [Exploring the Carbon Footprint of Hugging Face's ML Models: A Repository Mining Study](https://mlsanity.com/paper/2305.11164/) (2023)
- [Threats to Pre-trained Language Models: Survey and Taxonomy](https://mlsanity.com/paper/2202.06862/) (2022)
- [The Prevalence of Code Smells in Machine Learning projects](https://mlsanity.com/paper/2103.04146/) (2021)
- [Aspirations and Practice of Model Documentation: Moving the Needle with Nudging and Traceability](https://mlsanity.com/paper/2204.06425/) (2022)
- [EvilModel 2.0: Bringing Neural Network Models into Malware Attacks](https://mlsanity.com/paper/2109.04344/) (2021)
- [Challenges and Practices of Deep Learning Model Reengineering: A Case Study on Computer Vision](https://mlsanity.com/paper/2303.07476/) (2023)
- [Bug Characterization in Machine Learning-based Systems](https://mlsanity.com/paper/2307.14512/) (2023)
- [The Product Beyond the Model -- An Empirical Study of Repositories of Open-Source ML Products](https://mlsanity.com/paper/2308.04328/) (2023)
- [Scalpel: The Python Static Analysis Framework](https://mlsanity.com/paper/2202.11840/) (2022)
- [Understanding Flaky Tests: The Developer's Perspective](https://mlsanity.com/paper/1907.01466/) (2019)
- [Code Smells for Machine Learning Applications](https://mlsanity.com/paper/2203.13746/) (2022)
- [What Do Users Ask in Open-Source AI Repositories? An Empirical Study of GitHub Issues](https://mlsanity.com/paper/2303.09795/) (2023)
- [What Makes a Popular Academic AI Repository?](https://mlsanity.com/paper/2010.02472/) (2020)
- [Reusing Deep Neural Network Models through Model Re-engineering](https://mlsanity.com/paper/2304.00245/) (2023)
- [Prevalence of Code Smells in Reinforcement Learning Projects](https://mlsanity.com/paper/2303.10236/) (2023)

## Built on by (most cited first, 1 of 1)

- [Responsible AI in Open Ecosystems: Reconciling Innovation with Risk Assessment and Disclosure](https://mlsanity.com/paper/2409.19104/) (2024)

## Abstract (arXiv)

Developing and training deep learning models is expensive, so software engineers have begun to reuse pre-trained deep learning models (PTMs) and fine-tune them for downstream tasks. Despite the wide-spread use of PTMs, we know little about the corresponding software engineering behaviors and challenges. To enable the study of software engineering with PTMs, we present the PeaTMOSS dataset: Pre-Trained Models in Open-Source Software. PeaTMOSS has three parts: a snapshot of (1) 281,638 PTMs, (2) 27,270 open-source software repositories that use PTMs, and (3) a mapping between PTMs and the projects that use them. We challenge PeaTMOSS miners to discover software engineering practices around PTMs. A demo and link to the full dataset are available at: https://github.com/PurdueDualityLab/PeaTMOSS-Demos.